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ORIGINAL RESEARCH article

Front. Public Health

Sec. Aging and Public Health

Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1548999

Dying to pay:End-of-Life Medical Costs for Middle-Aged and Elderly Patients with Cardiovascular and Cerebrovascular Diseases

Provisionally accepted
  • Yan shan university, He bei, China

The final, formatted version of the article will be published soon.

    Objective: This study aims to investigate end-of-life healthcare expenditures among middle-aged and older patients with cardiovascular and cerebrovascular diseases, with a particular focus on the existence of the "nearing-death effect." Methods: Using inpatient discharge summary data from the Chinese National Medical Insurance Settlement Platform, we identified a cohort of middle-aged and older adults (aged 45 and above) diagnosed with cardiovascular and cerebrovascular diseases in Province H, China, during 2018-2019. Propensity Score Matching (PSM) was employed to examine differences in end-of-life healthcare expenditures between deceased and surviving patients. Robustness checks were performed using Multidimensional Fixed Effects (MDFE) and Difference-in-Differences Machine Learning (DDML).The findings reveal a substantial increase in end-of-life healthcare expenditures among patients with cardiovascular and cerebrovascular diseases. Specifically, Total Medical Costs, Comprehensive Service Fees, Diagnosis Fees, Treatment Fees, Pharmaceutical Fees, and Nursing Care Fees rose by 34.3%, 44.0%, 35.7%, 62.5%, 49.9%, and 46.8%, respectively, all statistically significant at the 1% level. These results highlight a pronounced escalation in healthcare expenditures associated with patient mortality.Among middle-aged and older patients with cardiovascular and cerebrovascular diseases, healthcare expenditures exhibit a distinct "end-of-life effect," characterised by a sharp surge in medical spending during the final stages of life. This phenomenon underscores the intensive utilisation of medical resources at the end of life, markedly differing from healthcare expenditure patterns at other stages of life.

    Keywords: Cardiovascular and cerebrovascular diseases, end-of-life healthcare expenditures, Terminal stage, Propensity score matching (PSM), Difference-in-Differences Machine Learning (DDML)

    Received: 04 Jan 2025; Accepted: 27 Feb 2025.

    Copyright: © 2025 Hu, Zhao, Bian and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ying Li, Yan shan university, He bei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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